🤖 自动格式化代码 [skip ci]

src/chat/focus_chat/heartFC_chat.py

@@ -50,7 +50,6 @@ PROCESSOR_CLASSES = {
 logger = get_logger("hfc")  # Logger Name Changed
 
 
-
 class HeartFChatting:
     """
     管理一个连续的Focus Chat循环
@@ -100,9 +99,7 @@ class HeartFChatting:
         self._register_default_processors()
 
         self.action_manager = ActionManager()
-        self.action_planner = ActionPlanner(
+        self.action_planner = ActionPlanner(log_prefix=self.log_prefix, action_manager=self.action_manager)
-            log_prefix=self.log_prefix, action_manager=self.action_manager
-        )
         self.action_modifier = ActionModifier(action_manager=self.action_manager, chat_id=self.stream_id)
         self.action_observation = ActionObservation(observe_id=self.stream_id)
         self.action_observation.set_action_manager(self.action_manager)
@@ -360,7 +357,6 @@ class HeartFChatting:
                         formatted_time = f"{elapsed * 1000:.2f}毫秒" if elapsed < 1 else f"{elapsed:.2f}秒"
                         timer_strings.append(f"{name}: {formatted_time}")
 
-
                     logger.info(
                         f"{self.log_prefix} 第{self._current_cycle_detail.cycle_id}次思考,"
                         f"耗时: {self._current_cycle_detail.end_time - self._current_cycle_detail.start_time:.1f}秒, "
@@ -382,7 +378,7 @@ class HeartFChatting:
                         self.performance_logger.record_cycle(cycle_performance_data)
                     except Exception as perf_e:
                         logger.warning(f"{self.log_prefix} 记录性能数据失败: {perf_e}")
-                    
+
                     await asyncio.sleep(global_config.focus_chat.think_interval)
 
                 except asyncio.CancelledError:
@@ -494,7 +490,6 @@ class HeartFChatting:
                     )
                     traceback.print_exc()
 
-
         return all_plan_info
 
     async def _observe_process_plan_action_loop(self, cycle_timers: dict, thinking_id: str) -> dict:
@@ -528,7 +523,6 @@ class HeartFChatting:
                     logger.error(f"{self.log_prefix} 动作修改失败: {e}")
                     # 继续执行，不中断流程
 
-
             try:
                 all_plan_info = await self._process_processors(self.observations)
             except Exception as e:

src/chat/focus_chat/memory_activator.py

@@ -117,7 +117,6 @@ class MemoryActivator:
 
             # 添加新的关键词到缓存
             self.cached_keywords.update(keywords)
-            
 
         # 调用记忆系统获取相关记忆
         related_memory = await hippocampus_manager.get_memory_from_topic(

src/chat/normal_chat/normal_chat.py

@@ -28,6 +28,7 @@ willing_manager = get_willing_manager()
 
 logger = get_logger("normal_chat")
 
+
 class NormalChat:
     """
     普通聊天处理类，负责处理非核心对话的聊天逻辑。
@@ -61,7 +62,7 @@ class NormalChat:
 
         self.willing_amplifier = 1
         self.start_time = time.time()
-        
+
         self.mood_manager = mood_manager
         self.start_time = time.time()
 
@@ -77,7 +78,6 @@ class NormalChat:
         self.recent_replies = []
         self.max_replies_history = 20  # 最多保存最近20条回复记录
 
-
         # 添加回调函数，用于在满足条件时通知切换到focus_chat模式
         self.on_switch_to_focus_callback = on_switch_to_focus_callback
 
@@ -561,16 +561,14 @@ class NormalChat:
     async def reply_one_message(self, message: MessageRecv) -> None:
         # 回复前处理
         await self.relationship_builder.build_relation()
-        
+
         thinking_id = await self._create_thinking_message(message)
 
         # 如果启用planner，预先修改可用actions（避免在并行任务中重复调用）
         available_actions = None
         if self.enable_planner:
             try:
-                await self.action_modifier.modify_actions(
+                await self.action_modifier.modify_actions(mode="normal", message_content=message.processed_plain_text)
-                    mode="normal", message_content=message.processed_plain_text
-                )
                 available_actions = self.action_manager.get_using_actions_for_mode("normal")
             except Exception as e:
                 logger.warning(f"[{self.stream_name}] 获取available_actions失败: {e}")
@@ -647,7 +645,7 @@ class NormalChat:
             logger.info(f"[{self.stream_name}] 回复内容中没有文本，不发送消息")
             await self._cleanup_thinking_message_by_id(thinking_id)
             return False
-            
+
         # 发送回复 (不再需要传入 chat)
         first_bot_msg = await self._add_messages_to_manager(message, reply_texts, thinking_id)
 
@@ -954,6 +952,7 @@ class NormalChat:
         except Exception as e:
             logger.warning(f"[{self.stream_name}] 获取疲劳调整系数时出错: {e}")
             return 1.0  # 出错时返回正常系数
+
     async def _check_should_switch_to_focus(self) -> bool:
         """
         检查是否满足切换到focus模式的条件
@@ -1027,4 +1026,4 @@ def get_recent_message_stats(minutes: int = 30, chat_id: str = None) -> dict:
     bot_filter["user_id"] = bot_id
     bot_reply_count = count_messages(bot_filter)
 
-    return {"bot_reply_count": bot_reply_count, "total_message_count": total_message_count}
+    return {"bot_reply_count": bot_reply_count, "total_message_count": total_message_count}

src/chat/planner_actions/action_modifier.py

@@ -2,7 +2,6 @@ from typing import List, Optional, Any, Dict
 from src.chat.heart_flow.observation.observation import Observation
 from src.common.logger import get_logger
 from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservation
-from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
 from src.chat.message_receive.chat_stream import get_chat_manager
 from src.config.config import global_config
 from src.llm_models.utils_model import LLMRequest
@@ -62,10 +61,10 @@ class ActionModifier:
 
         removals_s1 = []
         removals_s2 = []
-        
+
         self.action_manager.restore_actions()
         all_actions = self.action_manager.get_using_actions_for_mode(mode)
-        
+
         message_list_before_now_half = get_raw_msg_before_timestamp_with_chat(
             chat_id=self.chat_stream.stream_id,
             timestamp=time.time(),
@@ -79,7 +78,7 @@ class ActionModifier:
             read_mark=0.0,
             show_actions=True,
         )
-        
+
         if message_content:
             chat_content = chat_content + "\n" + f"现在，最新的消息是：{message_content}"
 
@@ -104,14 +103,13 @@ class ActionModifier:
             self.action_manager.remove_action_from_using(action_name)
             logger.debug(f"{self.log_prefix}阶段一移除动作: {action_name}，原因: {reason}")
 
-
         # === 第二阶段：激活类型判定 ===
         if chat_content is not None:
             logger.debug(f"{self.log_prefix}开始激活类型判定阶段")
 
             # 获取当前使用的动作集（经过第一阶段处理）
             current_using_actions = self.action_manager.get_using_actions_for_mode(mode)
-            
+
             # 获取因激活类型判定而需要移除的动作
             removals_s2 = await self._get_deactivated_actions_by_type(
                 current_using_actions,
@@ -123,7 +121,7 @@ class ActionModifier:
             for action_name, reason in removals_s2:
                 self.action_manager.remove_action_from_using(action_name)
                 logger.debug(f"{self.log_prefix}阶段二移除动作: {action_name}，原因: {reason}")
-        
+
         # === 统一日志记录 ===
         all_removals = removals_s1 + removals_s2
         if all_removals:
@@ -141,11 +139,9 @@ class ActionModifier:
                     associated_types_str = ", ".join(data["associated_types"])
                     reason = f"适配器不支持（需要: {associated_types_str}）"
                     type_mismatched_actions.append((action_name, reason))
-                    logger.debug(
+                    logger.debug(f"{self.log_prefix}决定移除动作: {action_name}，原因: {reason}")
-                        f"{self.log_prefix}决定移除动作: {action_name}，原因: {reason}"
-                    )
         return type_mismatched_actions
-    
+
     async def _get_deactivated_actions_by_type(
         self,
         actions_with_info: Dict[str, Any],
@@ -166,7 +162,7 @@ class ActionModifier:
 
         # 分类处理不同激活类型的actions
         llm_judge_actions = {}
-        
+
         actions_to_check = list(actions_with_info.items())
         random.shuffle(actions_to_check)
 
@@ -193,7 +189,7 @@ class ActionModifier:
 
             elif activation_type == "llm_judge":
                 llm_judge_actions[action_name] = action_info
-            
+
             else:
                 logger.warning(f"{self.log_prefix}未知的激活类型: {activation_type}，跳过处理")
 
@@ -517,21 +513,23 @@ class ActionModifier:
             # 如果最近sec_thres_reply_num次都是reply，40%概率移除
             removal_probability = 0.4 / global_config.focus_chat.consecutive_replies
             if random.random() < removal_probability:
-                reason = f"连续回复较多（最近{sec_thres_reply_num}次全是reply，{removal_probability:.2f}概率移除，触发移除）"
+                reason = (
+                    f"连续回复较多（最近{sec_thres_reply_num}次全是reply，{removal_probability:.2f}概率移除，触发移除）"
+                )
                 removals.append(("reply", reason))
         elif len(last_max_reply_num) >= one_thres_reply_num and all(last_max_reply_num[-one_thres_reply_num:]):
             # 如果最近one_thres_reply_num次都是reply，20%概率移除
             removal_probability = 0.2 / global_config.focus_chat.consecutive_replies
             if random.random() < removal_probability:
-                reason = f"连续回复检测（最近{one_thres_reply_num}次全是reply，{removal_probability:.2f}概率移除，触发移除）"
+                reason = (
+                    f"连续回复检测（最近{one_thres_reply_num}次全是reply，{removal_probability:.2f}概率移除，触发移除）"
+                )
                 removals.append(("reply", reason))
         else:
             logger.debug(f"{self.log_prefix}连续回复检测：无需移除reply动作，最近回复模式正常")
 
         return removals
 
-
-
     def get_available_actions_count(self) -> int:
         """获取当前可用动作数量（排除默认的no_action）"""
         current_actions = self.action_manager.get_using_actions_for_mode("normal")
@@ -545,4 +543,4 @@ class ActionModifier:
         if available_count == 0:
             logger.debug(f"{self.log_prefix} 没有可用动作，跳过规划")
             return True
-        return False
+        return False

src/chat/planner_actions/planner_focus.py

@@ -83,9 +83,7 @@ class ActionPlanner:
             request_type="focus.planner",  # 用于动作规划
         )
 
-    async def plan(
+    async def plan(self, all_plan_info: List[InfoBase], loop_start_time: float) -> Dict[str, Any]:
-        self, all_plan_info: List[InfoBase],loop_start_time: float
-    ) -> Dict[str, Any]:
         """
         规划器 (Planner): 使用LLM根据上下文决定做出什么动作。
 

src/chat/replyer/default_generator.py

@@ -98,7 +98,6 @@ class DefaultReplyer:
         self.log_prefix = "replyer"
         self.request_type = request_type
 
-
         if model_configs:
             self.express_model_configs = model_configs
         else:
@@ -470,7 +469,13 @@ class DefaultReplyer:
         duration = end_time - start_time
         return name, result, duration
 
-    async def build_prompt_reply_context(self, reply_data=None, available_actions: List[str] = None, enable_timeout: bool = False, enable_tool: bool = True) -> str:
+    async def build_prompt_reply_context(
+        self,
+        reply_data=None,
+        available_actions: List[str] = None,
+        enable_timeout: bool = False,
+        enable_tool: bool = True,
+    ) -> str:
         """
         构建回复器上下文
 
@@ -537,10 +542,16 @@ class DefaultReplyer:
 
         # 并行执行四个构建任务
         task_results = await asyncio.gather(
-            self._time_and_run_task(self.build_expression_habits(chat_talking_prompt_half, target), "build_expression_habits"),
+            self._time_and_run_task(
-            self._time_and_run_task(self.build_relation_info(reply_data, chat_talking_prompt_half), "build_relation_info"),
+                self.build_expression_habits(chat_talking_prompt_half, target), "build_expression_habits"
+            ),
+            self._time_and_run_task(
+                self.build_relation_info(reply_data, chat_talking_prompt_half), "build_relation_info"
+            ),
             self._time_and_run_task(self.build_memory_block(chat_talking_prompt_half, target), "build_memory_block"),
-            self._time_and_run_task(self.build_tool_info(reply_data, chat_talking_prompt_half, enable_tool=enable_tool), "build_tool_info"),
+            self._time_and_run_task(
+                self.build_tool_info(reply_data, chat_talking_prompt_half, enable_tool=enable_tool), "build_tool_info"
+            ),
         )
 
         # 处理结果

src/config/official_configs.py

@@ -273,7 +273,6 @@ class MessageReceiveConfig(ConfigBase):
 class NormalChatConfig(ConfigBase):
     """普通聊天配置类"""
 
-
     willing_mode: str = "classical"
     """意愿模式"""
 
@@ -290,7 +289,6 @@ class NormalChatConfig(ConfigBase):
     """是否启用动作规划器"""
 
 
-
 @dataclass
 class FocusChatConfig(ConfigBase):
     """专注聊天配置类"""
@@ -348,7 +346,7 @@ class ToolConfig(ConfigBase):
 @dataclass
 class EmojiConfig(ConfigBase):
     """表情包配置类"""
-    
+
     emoji_chance: float = 0.6
     """发送表情包的基础概率"""
 

src/person_info/relationship_fetcher.py

@@ -106,7 +106,7 @@ class RelationshipFetcher:
             await self._extract_single_info(person_id, info_type, person_name)
 
         relation_info = self._organize_known_info()
-        if short_impression and relation_info: 
+        if short_impression and relation_info:
             relation_info = f"你对{person_name}的印象是：{short_impression}。具体来说：{relation_info}"
         elif short_impression:
             relation_info = f"你对{person_name}的印象是：{short_impression}"

src/plugin_system/apis/generator_api.py

@@ -92,9 +92,7 @@ async def generate_reply(
     """
     try:
         # 获取回复器
-        replyer = get_replyer(
+        replyer = get_replyer(chat_stream, chat_id, model_configs=model_configs, request_type=request_type)
-            chat_stream, chat_id, model_configs=model_configs, request_type=request_type
-        )
         if not replyer:
             logger.error("[GeneratorAPI] 无法获取回复器")
             return False, []

src/tools/tool_executor.py

@@ -45,7 +45,7 @@ class ToolExecutor:
         self.chat_id = chat_id
         self.chat_stream = get_chat_manager().get_stream(self.chat_id)
         self.log_prefix = f"[{get_chat_manager().get_stream_name(self.chat_id) or self.chat_id}]"
-        
+
         self.llm_model = LLMRequest(
             model=global_config.model.tool_use,
             request_type="tool_executor",